| 四川地区多模式2m温度预报性能分析及集成方法研究 |
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| 引用本文： | 冯良敏,周秋雪,陈朝平,范江琳.四川地区多模式2m温度预报性能分析及集成方法研究[J].高原山地气象研究,2019,39(2):30-36. |
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| 作者姓名： | 冯良敏 周秋雪 陈朝平 范江琳 |
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| 作者单位： | 1. 四川省气象台, 成都 610072; |
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| 基金项目： | 高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金项目(2018-青年-02,2018-重点-06) |
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| 摘 要： | 本文通过分析2017年9~12月四川地区ECMWF(European Centre for Medium-Range Weather Forecasting)细网格模式、GRAPES_GFS(Global and Regional Assimilation and Prediction System)全球模式和西南区域模式(South West Center-WRF ADAS Real-time Modeling System, SWCWARMS)2m温度168h预报时效内的系统性偏差特征,采用滑动双权重平均法分别对三种模式温度预报产品进行偏差订正,并集成得到各时效2m温度的订正场,结果表明:(1)三种模式的预报存在明显的日变化,整体上EC模式的预报最优。(2)三种模式对于低温和高温的预报,在全省均大致呈现负的系统性误差,特别在高原及过渡区表现的尤为明显。(3)订正后三种模式的预报准确率显著提高,均方根误差减小1.4~2.5℃,大部分地区平均误差维持在±0.5℃之间,在系统性偏差较大的地区,订正效果更好。(4)两种集成方案预报结果接近,且均优于三种模式的订正预报。
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| 关 键 词： | 2m温度 偏差订正 双权重滑动平均 多模式集成 |
| 收稿时间： | 2019-05-04 |
Performance Analysis and Study on Integrated Method of Multi-model 2m Temperature in Sichuan Province |
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| FENG Liangmin,ZHOU Qiuxue,CHEN Chaoping,FAN Jianglin.Performance Analysis and Study on Integrated Method of Multi-model 2m Temperature in Sichuan Province[J].Plateau and Mountain Meteorology Research,2019,39(2):30-36. |
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| Authors: | FENG Liangmin ZHOU Qiuxue CHEN Chaoping FAN Jianglin |
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| Institution: | 1. Sichuan Meteorological Observatory, Chengdu 610072, China;2. Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China |
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| Abstract: | Based on the ECMWF,Grapes_GFS and SWCWARMS 2m temperature forecast within 168 hour-period of validity from September to December 2017,the bi-weight moving mean method is conducted separately to each model to reduce the systematic deviation.Then an integrated 2m temperature correction field is obtained from the three models.The results show that:(1) There is a clear daily change in the forecast of all three models,and the forecast of the EC is optimal.(2) The forecast on both the lowest and highest temperature in the three models are generally of negative systematic bias,especially within the plateau and transitional areas.(3) The accuracy rate of forecasting has been significantly improved,with the root mean square error decreasing by about 1.4-2.5℃,and the average error between ± 0.5℃ in most areas.Generally,the bias correction is more effective in areas with large deviations.(4) The variable-weighted and the equal-weighted integration schemes have similar effects,and both of them are better than the correction field of the three models. |
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| Keywords: | 2m temperature bias correction bi-weight moving mean multi-model integration |
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